Convert dataset to Parquet

README.md

@@ -20,6 +20,7 @@ task_ids:
 paperswithcode_id: lener-br
 pretty_name: leNER-br
 dataset_info:
+  config_name: lener_br
   features:
   - name: id
     dtype: string
@@ -42,21 +43,30 @@ dataset_info:
           '10': I-LEGISLACAO
           '11': B-JURISPRUDENCIA
           '12': I-JURISPRUDENCIA
-  config_name: lener_br
   splits:
   - name: train
-    num_bytes: 3984189
+    num_bytes: 3984173
     num_examples: 7828
   - name: validation
-    num_bytes: 719433
+    num_bytes: 719417
     num_examples: 1177
   - name: test
-    num_bytes: 823708
+    num_bytes: 823692
     num_examples: 1390
-  download_size: 2983137
-  dataset_size: 5527330
+  download_size: 1033001
+  dataset_size: 5527282
 tags:
 - legal
+configs:
+- config_name: lener_br
+  data_files:
+  - split: train
+    path: lener_br/train-*
+  - split: validation
+    path: lener_br/validation-*
+  - split: test
+    path: lener_br/test-*
+  default: true
 ---
 
 # Dataset Card for leNER-br

lener_br.py

@@ -1,160 +0,0 @@
-# coding=utf-8
-# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""LeNER-Br dataset"""
-
-
-import datasets
-
-
-logger = datasets.logging.get_logger(__name__)
-
-
-_CITATION = """
-@inproceedings{luz_etal_propor2018,
-    author = {Pedro H. {Luz de Araujo} and Te\'{o}filo E. {de Campos} and
-    Renato R. R. {de Oliveira} and Matheus Stauffer and
-    Samuel Couto and Paulo Bermejo},
-    title = {{LeNER-Br}: a Dataset for Named Entity Recognition in {Brazilian} Legal Text},
-    booktitle = {International Conference on the Computational Processing of Portuguese ({PROPOR})},
-    publisher = {Springer},
-    series = {Lecture Notes on Computer Science ({LNCS})},
-    pages = {313--323},
-    year = {2018},
-    month = {September 24-26},
-    address = {Canela, RS, Brazil},
-    doi = {10.1007/978-3-319-99722-3_32},
-    url = {https://cic.unb.br/~teodecampos/LeNER-Br/},
-}
-"""
-
-_DESCRIPTION = """
-LeNER-Br is a Portuguese language dataset for named entity recognition
-applied to legal documents. LeNER-Br consists entirely of manually annotated
-legislation and legal cases texts and contains tags for persons, locations,
-time entities, organizations, legislation and legal cases.
-To compose the dataset, 66 legal documents from several Brazilian Courts were
-collected. Courts of superior and state levels were considered, such as Supremo
-Tribunal Federal, Superior Tribunal de Justiça, Tribunal de Justiça de Minas
-Gerais and Tribunal de Contas da União. In addition, four legislation documents
-were collected, such as "Lei Maria da Penha", giving a total of 70 documents
-"""
-
-_HOMEPAGE = "https://cic.unb.br/~teodecampos/LeNER-Br/"
-
-_URL = "https://github.com/peluz/lener-br/raw/master/leNER-Br/"
-_TRAINING_FILE = "train/train.conll"
-_DEV_FILE = "dev/dev.conll"
-_TEST_FILE = "test/test.conll"
-
-
-class LenerBr(datasets.GeneratorBasedBuilder):
-    """LeNER-Br dataset"""
-
-    VERSION = datasets.Version("1.0.0")
-
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(name="lener_br", version=VERSION, description="LeNER-Br dataset"),
-    ]
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "id": datasets.Value("string"),
-                    "tokens": datasets.Sequence(datasets.Value("string")),
-                    "ner_tags": datasets.Sequence(
-                        datasets.features.ClassLabel(
-                            names=[
-                                "O",
-                                "B-ORGANIZACAO",
-                                "I-ORGANIZACAO",
-                                "B-PESSOA",
-                                "I-PESSOA",
-                                "B-TEMPO",
-                                "I-TEMPO",
-                                "B-LOCAL",
-                                "I-LOCAL",
-                                "B-LEGISLACAO",
-                                "I-LEGISLACAO",
-                                "B-JURISPRUDENCIA",
-                                "I-JURISPRUDENCIA",
-                            ]
-                        )
-                    ),
-                }
-            ),
-            supervised_keys=None,
-            homepage="https://cic.unb.br/~teodecampos/LeNER-Br/",
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        """Returns SplitGenerators."""
-        urls_to_download = {
-            "train": f"{_URL}{_TRAINING_FILE}",
-            "dev": f"{_URL}{_DEV_FILE}",
-            "test": f"{_URL}{_TEST_FILE}",
-        }
-        downloaded_files = dl_manager.download_and_extract(urls_to_download)
-
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={"filepath": downloaded_files["train"], "split": "train"},
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                gen_kwargs={"filepath": downloaded_files["dev"], "split": "validation"},
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={"filepath": downloaded_files["test"], "split": "test"},
-            ),
-        ]
-
-    def _generate_examples(self, filepath, split):
-        """Yields examples."""
-
-        logger.info("⏳ Generating examples from = %s", filepath)
-
-        with open(filepath, encoding="utf-8") as f:
-
-            guid = 0
-            tokens = []
-            ner_tags = []
-
-            for line in f:
-                if line == "" or line == "\n":
-                    if tokens:
-                        yield guid, {
-                            "id": str(guid),
-                            "tokens": tokens,
-                            "ner_tags": ner_tags,
-                        }
-                        guid += 1
-                        tokens = []
-                        ner_tags = []
-                else:
-                    splits = line.split(" ")
-                    tokens.append(splits[0])
-                    ner_tags.append(splits[1].rstrip())
-
-            # last example
-            yield guid, {
-                "id": str(guid),
-                "tokens": tokens,
-                "ner_tags": ner_tags,
-            }

lener_br/test-00000-of-00001.parquet

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+size 161083

lener_br/train-00000-of-00001.parquet

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+size 739992

lener_br/validation-00000-of-00001.parquet

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+size 131926